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Generative AI with LangChain

You're reading from   Generative AI with LangChain Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781835083468
Length 368 pages
Edition 1st Edition
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Author (1):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
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Table of Contents (13) Chapters Close

Preface 1. What Is Generative AI? 2. LangChain for LLM Apps FREE CHAPTER 3. Getting Started with LangChain 4. Building Capable Assistants 5. Building a Chatbot Like ChatGPT 6. Developing Software with Generative AI 7. LLMs for Data Science 8. Customizing LLMs and Their Output 9. Generative AI in Production 10. The Future of Generative Models 11. Other Books You May Enjoy
12. Index

To get the most out of this book

To fully benefit from the value this book offers, it is essential to have at least a foundational understanding of Python. Additionally, having some basic knowledge of machine learning or neural networks can be helpful, though it is not required. Please be sure to carefully follow the instructions in Chapter 3 for setting up your Python environment using one of the popular tools, and for obtaining your access keys for OpenAI and other providers.

Work with notebooks and projects

The code for this book is hosted on GitHub at https://github.com/benman1/generative_ai_with_langchain. In the repository, you will find directories for each chapter, containing the notebooks and projects discussed in the book. As mentioned earlier, please ensure you follow the instructions for installing the necessary dependencies as outlined in Chapter 3 before working with the code. If you have any questions or concerns, please ask a question on Discord or raise an issue on GitHub.

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781835083468.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

from langchain.chains import LLMCheckerChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7)
text = "What type of mammal lays the biggest eggs?"

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

fromimport pandasai.llm.openai  OpenAI
llm = OpenAI(api_token="YOUR_API_TOKEN")
pandas_ai = PandasAI(llm)

Any command-line input or output is written as follows:

pip install -r requirements.txt

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “Select System info from the Administration panel.”

Warnings or important notes appear like this.

Tips and tricks appear like this.

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